Halmstad Intelligent Home - Capabilities and Opportunities

Lundström, Jens; Morais, Wagner Ourique de; Menezes, Maria Luiza Recena; Gabrielli, Celeste; Bentes, João; Sant'Anna, Anita Pinheiro; Synnott, Jonathan; Nugent, Christopher

doi:10.1007/978-3-319-51234-1_2

Cited by 13 publications

(13 citation statements)

References 8 publications

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“…A. Data Data from Halmstad Intelligent Home [17] (HINT) was acquired for this work. HINT is a sensor-equipped home able to capture occupancy, movement, and interactions.…”

Section: Methodsmentioning

confidence: 99%

Stability Analysis of the t-SNE Algorithm for Human Activity Pattern Data

Hamad

Järpe

Lundström³

2018

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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“…A. Data Data from Halmstad Intelligent Home [17] (HINT) was acquired for this work. HINT is a sensor-equipped home able to capture occupancy, movement, and interactions.…”

Section: Methodsmentioning

confidence: 99%

Stability Analysis of the t-SNE Algorithm for Human Activity Pattern Data

Hamad

Järpe

Lundström³

2018

2018 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

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“…Smart environments were developed to help older people or people who are suffering from some degenerative disorders (i.e., dementia) to maintain their independence in daily life. This is the case of the Halmstad Intelligent Home (HINT) at Halmstad University (Sweden), where a realistic home environment is provided for underpinning innovations and research studies relating to human behaviour analysis [38]. HINT, an apartment of 50 m 2 built, is supplied with a variety of thermal cameras and sensors (PIR, pressure, door contact, contact/touch, and others) capable with supporting (i) emergency detection and on-time response, (ii) detection of deviating behaviour patterns, and (iii) healthcare monitoring [38].…”

Section: Experiments Descriptionmentioning

confidence: 99%

Simulated Data to Estimate Real Sensor Events—A Poisson-Regression-Based Modelling

et al. 2020

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Automatic detection and recognition of Activities of Daily Living (ADL) are crucial for providing effective care to frail older adults living alone. A step forward in addressing this challenge is the deployment of smart home sensors capturing the intrinsic nature of ADLs performed by these people. As the real-life scenario is characterized by a comprehensive range of ADLs and smart home layouts, deviations are expected in the number of sensor events per activity (SEPA), a variable often used for training activity recognition models. Such models, however, rely on the availability of suitable and representative data collection and is habitually expensive and resource-intensive. Simulation tools are an alternative for tackling these barriers; nonetheless, an ongoing challenge is their ability to generate synthetic data representing the real SEPA. Hence, this paper proposes the use of Poisson regression modelling for transforming simulated data in a better approximation of real SEPA. First, synthetic and real data were compared to verify the equivalence hypothesis. Then, several Poisson regression models were formulated for estimating real SEPA using simulated data. The outcomes revealed that real SEPA can be better approximated ( R pred 2 = 92.72 % ) if synthetic data is post-processed through Poisson regression incorporating dummy variables.

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“…The H 2 Al design was also influenced by systems used in other similar smart home environments that focus on research on activity recognition and support of daily activities for persons with cognitive or physical impairments, such as the SERG lab at Ulster University (UK) [18], the Halmstad Intelligent Home at Halmstad University (Sweden) [19] and the Smart Home Lab at University of Jaen (Spain) [20]. These labs collaborate in a European Marie Skłodowska Curie Action named Remind (The use of computational techniques to Improve compliance to reminders within smart environments) that aims at ultimately setting up experiments across multiple labs [21].…”

Section: Remind-sensorcentralmentioning

confidence: 99%

H2Al—The Human Health and Activity Laboratory

Synnes¹,

Lilja²,

Nyman³

et al. 2018

UCAmI 2018

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The Human Health and Activity Laboratory (H2Al) is a new research facility at Luleå University of Technology implemented during 2018 as a smart home environment in an educational training apartment for nurses and therapists at the Luleå campus. This paper presents the design and implementation of the lab together with a discussion on potential impact. The aim is to identify and overcome economical, technical and social barriers to achieve an envisioned good and equal health and welfare within and from home environments. The lab is equipped with multiple sensor and actuator systems in the environment, worn by persons and based on digital information. The systems will allow for advanced capture, filtering, analysis and visualization of research data such as A/V, EEG, ECG, EMG, GSR, respiration and location while being able to detect falls, sleep apnea and other critical health and wellbeing issues. The resulting studies will be aimed towards supporting and equipping future home environments and care facilities, spanning from temporary care to primary care at hospitals, with technologies for activity and critical health and wellness issue detection. The work will be conducted at an International level and within a European context, based on a collaboration with other smart labs, such that experiments can be replicated at multiple sites. This paper presents some initial lessons learnt including design, setup and configuration for comparison of sensor placements and configurations as well as analytical methods.

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Halmstad Intelligent Home - Capabilities and Opportunities

Cited by 13 publications

References 8 publications

Stability Analysis of the t-SNE Algorithm for Human Activity Pattern Data

Stability Analysis of the t-SNE Algorithm for Human Activity Pattern Data

Simulated Data to Estimate Real Sensor Events—A Poisson-Regression-Based Modelling

H2Al—The Human Health and Activity Laboratory

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